Cross-Attention Enables Deep Learning on Limited Omics-Imaging-Clinical Data of 130 Lung Cancer Patients
Published in Cell Reports Methods, 2024
In this publication, we propose a cross-attention mechanism to integrate omics, imaging, and clinical data for improved analysis in lung cancer. The model effectively handles limited sample sizes and multi-modal data, demonstrating significant potential for personalized medicine in oncology.
Recommended citation: S. Verma, G. Magazzù, N. Eftekhari, A. Occhipinti, C. Angione, "Cross-attention enables deep learning on limited omics-imaging-clinical data of 130 lung cancer patients," Cell Reports Methods, 2024.
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